With video streaming becoming more and more popular, the number of devices that are capable of streaming videos over the Internet is growing. This leads to a heterogeneous device landscape with varying demands. Dynamic Adaptive Streaming over HTTP (DASH) offers an elegant solution to these demands. Smart adaptation logics are able to adjust the clients' streaming quality according to several (local) parameters. Recent research indicated benefits of blending Scalable Video Coding (SVC) with DASH, especially considering Future Internet architectures. However, except for a DASH dataset with a single SVC encoded video, no other datasets are publicly available. The contribution of this paper is two-fold. First, a DASH/SVC dataset, containing multiple videos at varying bitrates and spatial resolutions including 1080p, is presented. Second, a toolchain for multiplexing SVC encoded videos is provided, therefore making our results reproducible and allowing researchers to generate their own datasets.

Adaptive streaming strategies over HTTP allow to serve heterogeneous video users with varying demands. By providing different encoded versions (representations) of each video sequence on the server, clients have the freedom to select a representation that best fits their needs. While the topic of selecting a representation based on a pre-defined set is covered very well in the literature, the problem of how to properly select the representation set stored at the main server is usually an overlooked challenge. In this work, we provide an analysis on how the choice of representations on the server impacts the clients' quality. This is achieved by conducting NS-3 based simulations with a total of 10k users and up to 300 concurrent DASH clients for several recommended sets (e.g., Netflix, YouTube, and Apple), and measuring the experienced quality over a timespan of 24 hours. The results show that under heavy load (at peak hours) there is still room for improvement.

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